Merge branch 'feat/it18229912' into 'develop'

test: integrate prosody features

See merge request !25

Presently/presently/users/audioemotiontest.py

+import os
+from json_tricks import load
+import numpy as np
+import librosa
+from pydub import AudioSegment, effects
+import noisereduce as nr
+import tensorflow as tf
+import keras
+from keras.models import model_from_json
+from keras.models import load_model
+import matplotlib.pyplot as plt
+import pyaudio
+import wave
+from array import array
+import struct
+import time
+
+from os import path
+from pydub import AudioSegment
+
+#convert video files to .wav format
+# src = input("In: ")
+# dst = input("Out: ")
+# src = input("F:\\CDAP-PRESENTLY\\21_22-j-02\\Presently\\presently\\users\\media\\video\\22")
+# dst = input("F:\\CDAP-PRESENTLY\\21_22-j-02\\Presently\\presently\\users\\media\\audio")
+
+# sound = AudioSegment.from_mp3(src)
+# sound.export(dst, format="wav")
+
+saved_model_path = "F:\\CDAP-PRESENTLY\\21_22-j-02\\Presently\\presently\\users\\models\\model8723.json"
+saved_weights_path = "F:\\CDAP-PRESENTLY\\21_22-j-02\\Presently\\presently\\users\\models\\model8723_weights.h5"
+audio_file = "F:\\CDAP-PRESENTLY\\21_22-j-02\\Presently\\presently\\media\\audio\\fearful.wav"
+
+with open(saved_model_path, 'r') as json_file:
+    json_savedModel = json_file.read()
+    
+model = tf.keras.models.model_from_json(json_savedModel)
+model.load_weights(saved_weights_path)
+
+model.compile(loss='categorical_crossentropy', 
+                optimizer='RMSProp', 
+                metrics=['categorical_accuracy'])
+
+# print(model.summary())
+
+def preprocess(file_path, frame_length = 2048, hop_length = 512):
+    _, sr = librosa.load(path = file_path, sr = None)
+    rawsound = AudioSegment.from_file(file_path, duration = None) 
+    normalizedsound = effects.normalize(rawsound, headroom = 5.0) 
+    normal_x = np.array(normalizedsound.get_array_of_samples(), dtype = 'float32')         
+    # final_x = nr.reduce_noise(normal_x, sr=sr, use_tensorflow=True)
+    final_x = nr.reduce_noise(normal_x, sr=sr)
+        
+        
+    f1 = librosa.feature.rms(final_x, frame_length=frame_length, hop_length=hop_length, center=True, pad_mode='reflect').T 
+    f2 = librosa.feature.zero_crossing_rate(final_x, frame_length=frame_length, hop_length=hop_length,center=True).T 
+    f3 = librosa.feature.mfcc(final_x, sr=sr, S=None, n_mfcc=13, hop_length = hop_length).T
+    X = np.concatenate((f1, f2, f3), axis = 1)
+    
+    X_3D = np.expand_dims(X, axis=0)
+    
+    return X_3D
+
+emotions = {
+    0 : 'neutral',
+    1 : 'calm',
+    2 : 'happy',
+    3 : 'sad',
+    4 : 'angry',
+    5 : 'fearful',
+    6 : 'disgust',
+    7 : 'suprised'   
+}
+emo_list = list(emotions.values())
+
+def is_silent(data):
+    return max(data) < 100
+
+RATE = 24414
+CHUNK = 512
+RECORD_SECONDS = 7.1
+
+FORMAT = pyaudio.paInt32
+CHANNELS = 1
+WAVE_OUTPUT_FILE = "F:\\CDAP-PRESENTLY\\21_22-j-02\\Presently\\presently\\users\\models\\output.wav"
+
+p = pyaudio.PyAudio()
+stream = p.open(format=FORMAT,
+                channels=CHANNELS,
+                rate=RATE,
+                input=True,
+                frames_per_buffer=CHUNK)
+
+
+data = array('h', np.random.randint(size = 512, low = 0, high = 500))
+
+print("** session started")
+total_predictions = []
+tic = time.perf_counter()
+
+print("* recording...")
+frames = [] 
+data = np.nan
+
+timesteps = int(RATE / CHUNK * RECORD_SECONDS)
+for i in range(0, timesteps):
+        data = array('l', stream.read(CHUNK)) 
+        frames.append(data)
+
+        wf = wave.open(WAVE_OUTPUT_FILE, 'wb')
+        wf.setnchannels(CHANNELS)
+        wf.setsampwidth(p.get_sample_size(FORMAT))
+        wf.setframerate(RATE)
+        wf.writeframes(b''.join(frames))
+
+print("* done recording")
+
+x = preprocess(WAVE_OUTPUT_FILE)
+# x = WAVE_OUTPUT_FILE
+predictions = model.predict(x, use_multiprocessing=True)
+pred_list = list(predictions)
+pred_np = np.squeeze(np.array(pred_list).tolist(), axis=0)
+total_predictions.append(pred_np)
+    
+fig = plt.figure(figsize = (10, 2))
+plt.bar(emo_list, pred_np, color = 'darkturquoise')
+plt.ylabel("Probabilty (%)")
+plt.show()
+    
+max_emo = np.argmax(predictions)
+print('max emotion:', emotions.get(max_emo,-1))
+    
+print(100*'-')
+    
+toc = time.perf_counter()
+stream.stop_stream()
+stream.close()
+p.terminate()
+wf.close()
+print('** session ended')
+
+total_predictions_np =  np.mean(np.array(total_predictions).tolist(), axis=0)
+fig = plt.figure(figsize = (10, 5))
+plt.bar(emo_list, total_predictions_np, color = 'indigo')
+plt.ylabel("Mean probabilty (%)")
+plt.title("Session Summary")
+plt.show()
+
+print(f"Emotions analyzed for: {(toc - tic):0.4f} seconds")

Presently/presently/users/emotiondetectionvideo.py

@@ -190,4 +190,4 @@ def detect_emotions(location_videofile, NumberofFrames):
     return max(arry, key=arry.get), arry
 
 emo, arr1 = detect_emotions(location_videofile, 300)
-print(emo, arr1)
\ No newline at end of file
+# print(emo, arr1)
\ No newline at end of file

Presently/presently/users/prosodydetectionaudio.py

 import myprosody as mysp
 import pickle
 
-p="suun3" 
+p="happy" 
 c="F:\\CDAP-PRESENTLY\\21_22-j-02\\Presently\\presently\\users\\myprosody" 
 
+mysp.myspsyl(p,c)
+mysp.mysppaus(p,c)
+mysp.myspsr(p,c)
+mysp.myspatc(p,c)
+mysp.myspst(p,c)
+mysp.myspod(p,c)
+mysp.myspbala(p,c)
+mysp.myspf0mean(p,c)
+mysp.myspf0sd(p,c)
+mysp.myspf0med(p,c)
+mysp.myspf0min(p,c)
+mysp.myspf0max(p,c)
+mysp.myspf0q25(p,c)
+mysp.myspf0q75(p,c)
 
+mysp.mysptotal(p,c)
 mysp.myspgend(p,c)
 mysp.mysppron(p,c)
 mysp.myprosody(p,c)

Presently/presently/users/templates/users/test.html

@@ -3,11 +3,13 @@
 {% load static %}
 
 {% block content %}
+    <h2> {{ var1 }} {{ var2 }}</h2>
+
     <!-- {% for key, value in predictions.items %}
             <h2>{{ forloop.counter }}. {{ key }} : {{ value }}</h2>
         {% endfor %} -->
     
-    <div class="container">
+    <!-- <div class="container">
         <div class="heading-text heading-line text-center mt-5">
             <h4>Summery of emotions</h4>
         </div>
@@ -35,5 +37,5 @@
         <h5 class="py-5">Waiting...</h5>
             
     
-    </div>
+    </div> -->
 {% endblock %}
\ No newline at end of file

Presently/presently/users/views.py

@@ -10,13 +10,14 @@ from .models import Video
 from django.template import Template, Context
 import datetime
 from . import emotiondetectionvideo
+from . import emotiondetectionaudio
 
 def test(request):
-    emotion= emotiondetectionvideo.emo
-    arr= emotiondetectionvideo.arr1
+    var1= emotiondetectionaudio.emo_list
+    var2= emotiondetectionaudio.total_predictions_np
     context= {
-        'maxemotion': emotion,
-        'predictions': arr
+        'var1': var1,
+        'var2': var2
     }
     return render(request, 'users/test.html',context)